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Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights

Author

Listed:
  • Keith Kranker
  • Laura Blue
  • Lauren Vollmer Forrow

Abstract

This study describes a novel method to reweight a comparison group used for causal inference, so the group is similar to a treatment group on observable characteristics yet avoids highly variable weights that would limit statistical power.

Suggested Citation

  • Keith Kranker & Laura Blue & Lauren Vollmer Forrow, "undated". "Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights," Mathematica Policy Research Reports fbcf08b2933a464c8af976325, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:fbcf08b2933a464c8af9763259acd3ab
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    File URL: https://www.tandfonline.com/doi/full/10.1080/00031305.2020.1737229
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    Cited by:

    1. Song, Zisheng, 2021. "The capitalization of school quality in rents in the Beijing housing market: A propensity score method," Working Paper Series 21/7, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    2. Kraft, Kornelius & Lammers, Alexander, 2021. "The Effects of Reforming a Federal Employment Agency on Labor Demand," IZA Discussion Papers 14629, Institute of Labor Economics (IZA).
    3. Ben Jann, 2021. "Entropy balancing as an estimation command," University of Bern Social Sciences Working Papers 39, University of Bern, Department of Social Sciences, revised 16 Aug 2021.

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